Diagnosis of Hybrid Systems Using Hybrid Particle Petri Nets: Theory and Application on a Planetary Rover
Abstract
This chapter presents a new methodology to perform health monitoring of hybrid systems under uncertainty. Hybrid systems can be represented as multi-mode systems with hybrid automata. Diagnosers are generated from these hybrid automata using a new data structure in order to monitor both the behavior and degradation of such systems. After a review of the state of the art on different existing solutions for diagnosis of hybrid systems under uncertainty, we propose to introduce the Hybrid Particle Petri Nets (HPPN) modeling framework. The main advantage of HPPN is that they take into account knowledge-based uncertainty in the system representation and uncertainty in the diagnosis process. The HPPN-based diagnoser deals with occurrences of unobservable discrete events (such as fault events) and it is robust to false observations. It also estimates the continuous state of the system by using particle filtering. A methodology is proposed to perform model-based diagnosis on hybrid systems by using the HPPN modeling framework. The system diagnosis is computed at any time from a HPPN-based diagnoser and contains all the hypotheses over its past mode trajectory. Each hypothesis is valued with a belief degree and includes discrete and continuous state estimates, as well as the set of faults that occurred on the system up to the current time. The HPPN-based methodology is demonstrated with an application on the K11 planetary rover prototype developed by NASA Ames Research Center. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.
References
- 1.Balaban, E., Narasimhan, S., Daigle, M. J., Roychoudhury, I., Sweet A., Bond, C., et al. (2013). Development of a mobile robot test platform and methods for validation of prognostics-enabled decision making algorithms. International Journal of Prognostics and Health Management, 4(006), 1–19.Google Scholar
- 2.Basile, F., Chiacchio, P., & Tommasi, G. D. (2009). Fault diagnosis and prognosis in Petri nets by using a single generalized marking estimation. In 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Barcelona, Spain.Google Scholar
- 3.Bayoudh, M., Travé-Massuyes, L., & Olive, X. (2008). Hybrid systems diagnosis by coupling continuous and discrete event techniques. In IFAC World Congress, Seoul, Korea (pp. 7265–7270).Google Scholar
- 4.Biswas, G., Simon, G., Mahadevan, N., Narasimhan, S., Ramirez J., & Karsai, G. (2003). A robust method for hybrid diagnosis of complex systems. IFAC Proceedings Volumes, 36(5), 1023–1028.Google Scholar
- 5.Cardoso, J., Valette, R., & Dubois, D. (1999). Possibilistic Petri nets. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 29(5), 573–582.Google Scholar
- 6.Chanthery, E., & Ribot, P. (2013). An integrated framework for diagnosis and prognosis of hybrid systems. In 3rd Workshop on Hybrid Autonomous System, Rome, Italy.Google Scholar
- 7.Daigle, M., Roychoudhury, I., & Bregon, A. (2014). Integrated diagnostics and prognostics for the electrical power system of a planetary rover. In Annual Conference of the PHM Society, Fort Worth, TX, USA.Google Scholar
- 8.Daigle, M., Roychoudhury, I., & Bregon, A. (2015). Qualitative event-based diagnosis applied to a spacecraft electrical power distribution system. Control Engineering Practice, 38, 75–91.Google Scholar
- 9.Daigle, M., Sankararaman, S., & Kulkarni, C. S. (2015). Stochastic prediction of remaining driving time and distance for a planetary rover. In IEEE Aerospace Conference.Google Scholar
- 10.David, R., & Alla, H. (2005). Discrete, continuous, and hybrid Petri nets. New York: Springer.Google Scholar
- 11.Ding, S. X. (2014). Data-driven design of fault diagnosis and fault-tolerant control systems. New York: Springer.Google Scholar
- 12.Dotoli, M., Fanti, M. P., Giua, A., & Seatzu, C. (2008). Modelling systems by hybrid Petri nets: An application to supply chains. In Petri net, theory and applications. InTech.Google Scholar
- 13.Douc, R., & Cappé, O. (2005). Comparison of resampling schemes for particle filtering. In Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005. ISPA 2005 (pp. 64–69). New York: IEEE.Google Scholar
- 14.Doucet, A., & Johansen, A. M. (2009). A tutorial on particle filtering and smoothing: Fifteen years later. In Oxford handbook of nonlinear filtering. University Press.Google Scholar
- 15.Gaudel, Q., Chanthery, E., & Ribot, P. (2014). Health monitoring of hybrid systems using hybrid particle Petri nets. In Annual Conference of the PHM Society, Fort Worth, TX, USA.Google Scholar
- 16.Gaudel, Q., Chanthery, E., & Ribot, P. (2015). Hybrid particle Petri nets for systems health monitoring under uncertainty. International Journal of Prognostics and Health Management, 6(022), 1–20.Google Scholar
- 17.Gaudel, Q., Chanthery, E., Ribot, P., & Le Corronc, E. (2014). Hybrid systems diagnosis using modified particle Petri nets. In 25th International Workshop on Principles of Diagnosis, Graz, Austria.Google Scholar
- 18.Henzinger, T. (1996). The theory of hybrid automata. In 11th Annual IEEE Symposium on Logic in Computer Science (pp. 278–292).Google Scholar
- 19.Hofbaur, M. W., & Williams, B. C. (2002). Mode estimation of probabilistic hybrid systems. Lecture Notes in Computer Science, 2289, 253–266.Google Scholar
- 20.Horton, G., Kulkarni, V. G., Nicol, D. M., & Trivedi, K. S. (1998). Fluid stochastic petri nets: Theory, applications, and solution techniques. European Journal of Operational Research, 105(1), 184–201.Google Scholar
- 21.Jianxiong, W., Xudong, X., Xiaoying, B., Chuang, L., Xiangzhen, K., & Jianxiang, L. (2013). Performability analysis of avionics system with multilayer HM/FM using stochastic Petri nets. Chinese Journal of Aeronautics, 26(2), 363–377.Google Scholar
- 22.Jung, D., Ng, K. Y., Frisk, E., & Krysander, M. (2016). A combined diagnosis system design using model-based and data-driven methods. In 3rd Conference on Control and Fault-Tolerant Systems (SysTol) (pp. 177–182). New York: IEEE.Google Scholar
- 23.Khamassi, I., Sayed-Mouchaweh, M., Hammami, M., & Ghédira, K. (2016). Discussion and review on evolving data streams and concept drift adapting. In Evolving systems (pp. 1–23). Berlin: Springer.Google Scholar
- 24.Koutsoukos, X., Kurien, J., & Zhao, F. (2002). Monitoring and diagnosis of hybrid systems using particle filtering methods. In 15th International Symposium on Mathematical Theory of Networks and Systems, Notre Dame, IN, USA.Google Scholar
- 25.Lachat, D., Krebs, A., Thueer, T., & Siegwart, R. (2006). Antarctica rover design and optimization for limited power consumption. In 4th IFAC Symposium on Mechatronic Systems.Google Scholar
- 26.Leclercq, E., Medhi, E., Ould, S., & Lefebvre, D. (2008). Petri nets design based on neural networks. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 529–534).Google Scholar
- 27.Lesire, C., & Tessier, C. (2005). Particle Petri nets for aircraft procedure monitoring under uncertainty. In G. Cardio & P. Darondeau (Eds.), ICATPN 2005. Lecture notes in computer science (Vol. 3536, pp. 329–348). Heidelberg: Springer.Google Scholar
- 28.Li, T., Bolic, M., & Djuric, P. M. (2015). Resampling methods for particle filtering: Classification, implementation, and strategies. IEEE Signal Processing Magazine, 32(3), 70–86.Google Scholar
- 29.Narasimhan, S., Balaban, E., Daigle, M., Roychoudhury, I., Sweet, A., Celaya, J., et al. (2012). Autonomous decision making for planetary rovers using diagnostic and prognostic information. In 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Mexico (pp. 289–294).Google Scholar
- 30.Narasimhan, S., & Biswas, G. (2007). Model-based diagnosis of hybrid systems. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(3), 348–361.Google Scholar
- 31.Narasimhan, S., & Brownston, L. (2007). Hyde–A general framework for stochastic and hybrid model-based diagnosis. In 18th International Workshop on Principles of Diagnosis (Vol. 7, pp. 162–169).Google Scholar
- 32.Narasimhan, S., Dearden, R., & Benazera, E. (2004). Combining particle filters and consistency-based approaches for monitoring and diagnosis of stochastic hybrid systems. In 15th International Workshop on Principles of Diagnosis.Google Scholar
- 33.Ru, Y., & Hadjicostis, C. N. (2009). Fault diagnosis in discrete event systems modeled by partially observed Petri nets. Discrete Event Dynamic Systems, 19(4), 551–575.Google Scholar
- 34.Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Robotics and Automation, 40(9), 1555–1575.Google Scholar
- 35.Sayed-Mouchaweh, M., & Lughofer, E. (2015). Decentralized approach without a global model for fault diagnosis of discrete event systems. International Journal of Control, 88(11), 2228–2241. https://doi.org/10.1080/00207179.2015.1
- 36.Soldani, S., Combacau, M., Subias, A., & Thomas, J. (2007). On-board diagnosis system for intermittent fault: Application in automotive industry. In 7th IFAC International Conference on Fieldbuses and Networks in Industrial and Embedded Systems (Vol. 7-1, pp. 151–158). https://doi.org/10.3182/20071107-3-FR-3907.00021
- 37.Sweet, A., Gorospe, G., Daigle, M., Celaya, J. R., Balaban, E., Roychoudhury, I., et al. (2014). Demonstration of prognostics-enabled decision making algorithms on a hardware mobile robot test platform. In Annual Conference of the PHM Society, Fort Worth, TX, USA.Google Scholar
- 38.Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annual Reviews in Control, 42, 63–81.Google Scholar
- 39.Toubakh, H., & Sayed-Mouchaweh, M. (2016). Hybrid dynamic classifier for drift-like fault diagnosis in a class of hybrid dynamic systems: Application to wind turbine converters. Neurocomputing, 171, 1496–1516.Google Scholar
- 40.van der Merwe, R., Doucet, A., De Freitas, N., & Wan, E. (2000). The unscented particle filter. In Annual Conference on Neural Information Processing Systems (Vol. 2000, pp. 584–590).Google Scholar
- 41.Vianna, W. O. L., & Yoneyama, T. (2015). Interactive multiple-model application for hydraulic servovalve health monitoring. In Annual Conference of the PHM Society, Coronado, CA, USA.Google Scholar
- 42.Wang, W., Li, L., Zhou, D., & Liu, K. (2007). Robust state estimation and fault diagnosis for uncertain hybrid nonlinear systems. Nonlinear Analysis: Hybrid Systems, 1(1), 2–15.MathSciNetMATHGoogle Scholar
- 43.Yu, M., Wang, D., Luo, M., & Huang, L. (2011). Prognosis of hybrid systems with multiple incipient faults: Augmented global analytical redundancy relations approach. IEEE Transactions on Systems, Man, and Cybernetics, 41(Part A, 3), 540–551.Google Scholar
- 44.Zaidi, A., Zanzouri, N., & Tagina, N. (2006). Modelling and monitoring of hybrid systems by hybrid Petri nets. In 10th WSEAS International Conference on Systems.Google Scholar
- 45.Zhao, F., Koutsoukos, X., Haussecker, H., Reich, J., & Cheung, P. (2005). Monitoring and fault diagnosis of hybrid systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(6), 1225–1240.CrossRefGoogle Scholar
- 46.Zouaghi, L., Alexopoulos, A., Wagner, A., & Badreddin, E. (2011). Modified particle Petri nets for hybrid dynamical systems monitoring under environmental uncertainties. In IEEE/SICE International Symposium on System Integration (pp. 497–502).Google Scholar